The increase in electronic transactions in the last decade has brought about a substantial increase in fraudulent activity associated with electronic transactions. Credit card fraud, for example, involves theft using a credit card or similar payment means as a fraudulent financing source in a transaction.
Although credit card fraud amounts to a fraction of the all credit card transactions it accounts for huge financial losses, as many fraudulent transactions are large value transactions.
Credit card fraud detection involves monitoring transaction activity and detecting suspicious activity. Fraud detection techniques may be classified into two classes: statistical detection methods and artificial intelligence techniques. Examples of statistical data analysis techniques include data pre-processing techniques for detection, validation, error correction, and filling in of missing or incorrect data; calculation of various statistical parameters such as averages, quantiles, performance metrics, probability distributions, and so on; models and probability distributions of various business activities either in terms of various parameters or probability distributions; computing user profiles; time-series analysis of time-dependent data; clustering and classification to find patterns and associations among groups of data; and matching algorithms to detect anomalies in the behavior of transactions or users as compared to previously known models and profiles.
Predictive modelling is a process by which a model is created or chosen to try to best predict the probability of an outcome. A model may be chosen on the basis of detection theory to try and guess the probability of an outcome given a set amount of input data.
Predictive analytics relates to a variety of statistical techniques from modelling, machine learning, and data mining that analyse current and past facts to make predictions about future events.